Sensor data generation for controlling an autonomous vehicle

ABSTRACT

A method and an apparatus for generating sensor data for controlling an autonomous vehicle in an environment is provided, such as driverless transport vehicles in a factory for example. Sensor positions of static sensors and the sensors of autonomous vehicles are defined in a global coordinate system on the basis of an environment model, such as a BIM model for example. Sensor data is centrally generated in this global coordinate system for all sensors as a function of these sensor positions. The sensor data is then transformed into a local coordinate system of an autonomous vehicle and transferred for controlling the autonomous vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to EP Application No. 21181851.3,having a filing date of Jun. 25, 2021, the entire contents of which arehereby incorporated by reference.

FIELD OF TECHNOLOGY

The following relates to a computer-implemented method, an apparatus anda computer program product for generating sensor data for controlling anautonomous vehicle in an environment, such as a driverless transportvehicle (Automated Guided Vehicle; AGV) in a factory, for example.

BACKGROUND

As a rule, production processes in factories are simulated in advance ina virtual environment (virtual commissioning) in order to guarantee thecorrect interaction of all components before real commissioning of theplant, to optimize time sequences and to identify potential faults, suchas collisions for example. This presents a challenge, however, inparticular with production scenarios such as with individualizedproduction for example, because often production sequences are carriedout by means of dynamic components, in particular autonomous vehiclessuch as AGVs for example. AGVs can receive, for example, material at anyoutput points and transport it to any locations for further production.The behavior is thereby no longer deterministic however and for thisreason cannot be simulated beforehand. AGVs are no longer incorporatedin strictly predefined sequences and have to define the state of theirenvironment and optionally their position by way of sensors, therefore.As a rule this takes place by way of optical sensors such as cameras,LIDAR or laser distance measurement. For a realistic test of the AGVs invirtual commissioning and/or control of the AGVs in a real productionenvironment, data from these sensors has to be present accordingly to beable to determine a correct reaction of the AGVs to it.

SUMMARY

An aspect relates to provide sensor data for controlling an autonomousvehicle.

In accordance with a first aspect, embodiments of the invention relateto a computer-implemented method for generating sensor data forcontrolling an autonomous vehicle in an environment in which at leastone further autonomous vehicle is situated, with the following methodsteps:

a) reading in an environment model of the environment, wherein

-   -   the environment model has a global coordinate system and    -   the environment model comprises first sensor positions of static        sensors in the environment and environment information in this        global coordinate system and sensor parameters of the static        sensors,        b) generating a time stamp,        c) reading in second sensor positions and sensor parameters of        sensors, which are coupled to the autonomous vehicles, and        providing the second sensor positions in coordinates of the        global coordinate system,        d) generating sensor data for the static sensors and the sensors        of the autonomous vehicles as a function of the respective        sensor parameters and the respective first or second sensor        positions and taking into account the environment information in        coordinates of the global coordinate system, wherein the        generated time stamp is assigned to the generated sensor data,        e) selecting at least one of the autonomous vehicles,        f) transforming the generated sensor data into a local        coordinate system of the selected autonomous vehicle,        and        g) transmitting the transformed sensor data to a controller of        the selected autonomous vehicle for controlling the selected        autonomous vehicle in the environment as a function of the        transformed sensor data.

If it is not indicated otherwise in the following description, the terms“carry out”, “calculate”, “computer-aided”, “compute”, “define”,“generate”, “configure”, “reconstruct” and the like refer to actionsand/or processes and/or processing steps which change and/or generatedata and/or transfer the data into other data, it being possible for thedata to be presented or to exist in particular as physical variables,for example as electrical pulses. In particular, the expression“computer” should be interpreted as broadly as possible in order tocover in particular all electronic devices with data processingproperties. Computers can thus be for example personal computers,servers, programmable controllers (PLC), handheld computer systems,pocket PC devices, mobile phones and other communications devices, whichcan process data with the aid of a computer, processors and otherelectronic devices for data processing.

In connection with embodiments of the invention a “storage unit” can betaken to mean, for example, a volatile storage facility in the form ofRandom Access Memory (RAM) or a permanent storage facility such as ahard drive or a data carrier.

In connection with embodiments of the invention a “module” can be takento mean, for example, a processor for storing program commands. Forexample, the processor is specifically adapted to execute the programcommands in such a way that the processor executes functions in order toimplement or achieve the inventive method or a step of the inventivemethod.

In connection with embodiments of the invention “provision”, inparticular in relation to data and/or information, can be taken to mean,for example, computer-aided provision. Provision takes place, forexample, via an interface, such as a network interface for example, acommunications interface or an interface to a storage unit. Appropriatedata and/or information can be transferred and/or transmitted and/orretrieved and/or received via an interface of this kind, for exampleduring provision.

A “technical system” can be taken to mean, in particular, a machine, adevice, or also a plant comprising a plurality of machines. For example,the technical system is a production machine or a machine tool.

In connection with embodiments of the invention an “environment” can be,for example, a building/structure, such as a factory or production plantfor example, or a transport infrastructure. The environment ischaracterized, for example, by environmental features or environmentinformation, such as routes, walls, corners, obstacles, etc. forexample.

In connection with embodiments of the invention an “environment model”can, in particular, be taken to mean a computer-aided/computer-readablemodel or simulation model, which comprises information, features and/orproperties of the environment. In particular the environment can besimulated or mapped in a computer-aided manner with an environmentmodel. For example, the environment model can be a structure model of astructure, an infrastructure model of a (transport) infrastructure or abuilding model of a building, such as a factory/production plant forexample. In embodiments, the environment model comprises environmentinformation, such as routes/paths, obstacles, etc. for example.Environment information can also be taken to mean structuredata/building data.

A “sensor” can, in particular, be taken to mean a detector, a transduceror measuring sensor or probe. A physical sensor is in particular ahardware component or a hardware part (hardware sensor), whichquantitatively detects/measures physical variables. The sensor outputs ameasured value or value of the measured variable. A static sensor has,in particular, a fixed position and/or a predefined field ofvision/field of view.

A “time stamp” can in particular be taken to mean a digital time stamp.A time stamp can be used in particular to assign a unique time to anevent. In embodiments, a globally applicable time stamp can be generatedand used.

It is an advantage of embodiments of the present invention that for agiven time, sensor data can be globally generated jointly andsimultaneously for a plurality of autonomous vehicles. The sensor datais generated on the basis of an environment model, which provides aglobal coordinate system. The sensor data generated in this way can beconverted for a single vehicle by including a changing sensor positionand optionally a time delay. For this, the generated sensor data istransformed into a local coordinate system of the autonomous vehicle.

Embodiments of the invention make it possible, in particular, togenerate sensor data for the control of autonomous vehicles. Forexample, the sensor data can be used for control and/or virtualcommissioning of AGVs in a factory or production plant. For virtualcommissioning the sensor data may be simulated in a computer-aidedmanner. Alternatively, the sensor data can also be centrally generatedfor real control of autonomous vehicles and transferred to individualautonomous vehicles via a communications network. A single autonomousvehicle can thus obtain information from sensors, which are not assignedto this vehicle or coupled thereto. This improves the field of vision ofthe vehicle in particular and therewith also the accuracy of control.

In one embodiment of the method, the environment model can be acomputer-readable building model.

In embodiments, the computer-readable building model is what is known asa BIM model (Building Information Modelling, BIM for short), in otherwords an information model of a building for a digital buildingmodeling. A BIM model may comprise sensor positions of staticallyinstalled sensors and a type of the respective sensor and further sensorparameters. The BIM model can be used to define a global coordinatesystem in which participating autonomous vehicles can move.

In a further embodiment of the method, the sensor data for the staticsensors and the sensors of the autonomous vehicles can be centrallygenerated for a time stamp.

For example, the sensor data can be generated on a central computingunit, which may be coupled to all available sensors for data exchange.Alternatively, the sensor data can be centrally simulated for allavailable sensors. The sensor data thereby exists centrally in a globalcoordinate system.

In a further embodiment of the method, the time stamp can be updated inpredefined time increments and the sensor data can be generated anew forthe updated time stamp.

Sensor data, which can be used for control of at least one autonomousvehicle, is thus generated for each time increment.

In a further embodiment of the method, the sensor data can betransformed into the local coordinate system of the selected autonomousvehicle by taking into account a transmission latency for transmittingthe sensor data to the selected autonomous vehicle.

As a result, sensor data can be predicted for predefined time increment.For example, a movement can be extrapolated in this way.

In a further embodiment of the method, the sensor data can betransformed into the local coordinate system of the selected autonomousvehicle as a function of generated sensor data, to which a precedingtime stamp is assigned.

As a result, for example a speed of a detected object can be determinedand be taken into account in the coordinate transformation.

In a further embodiment of the method, the sensor data can be generatedby means of a computer-aided sensor simulation.

Data from modeled sensors can be generated by means of a computer-aidedsensor simulation. A sensor simulation can be carried out in particularas a function of environment information, which is provided for exampleby the environment model. For example data, which a sensor detects at apredefined position as a function of a predefined sensor type, field ofvision, sensitivity, etc., can be simulated in a computer-aided mannerwith a sensor simulation. In other words, a computer-aided sensorsimulation supplies, for example, output values of a sensor, taking intoaccount the environment information. In particular, further information,such as for example speed of a vehicle, a steering movement, etc., canbe taken into account in a sensor simulation.

In a further embodiment of the method, a quality of at least some of thegenerated sensor data can be reduced and this modified sensor data canbe transformed into the local coordinate system of the selectedautonomous vehicle at a lower quality.

For example, a noise signal on the generated sensor data can bemodulated and/or a field of view of the respective sensor can be(artificially) restricted. The generated data can be modified in such away, in particular with sensor data generation by means of acomputer-aided sensor simulation, and can be presented morerealistically as a result.

In a further embodiment of the method, the generated sensor data can befiltered in the global coordinate system as a function of the positionof the selected autonomous vehicle and only the filtered sensor datatransformed into the local coordinate system of the selected autonomousvehicle and transferred to the selected autonomous vehicle.

This makes is possible, in particular, to transfer only that sensor datato the vehicle which is relevant to the vehicle.

In a further embodiment of the method, the static sensors and thesensors of the autonomous vehicles and/or the autonomous vehicles can beconnected together by a communications network.

For example, a communications network of this kind can be a 5G network.The sensor data can be centrally generated by way of the network andthen transferred to the respective vehicles.

In a further embodiment of the method, the selected autonomous vehiclecan be controlled in a simulation environment as a function of thetransformed sensor data.

For example, this can be a virtual commissioning of the autonomousvehicle. For this, a movement of the autonomous vehicle in theenvironment can be simulated, wherein the simulated sensor data is usedto control reactions of the autonomous vehicle.

In a further embodiment of the method, the environment can be a factoryand the autonomous vehicles can be driverless transport vehicles(Automated Guided Vehicles, AGVs).

In accordance with a second aspect, embodiments of the invention relateto an apparatus for generating sensor data for controlling an autonomousvehicle in an environment in which at least one further autonomousvehicle is situated, wherein the apparatus comprises:

a) a first interface, which is adapted to read in an environment modelof the environment, wherein

-   -   the environment model has a global coordinate system and    -   the environment model comprises first sensor positions of static        sensors in the environment and environment information in this        global coordinate system and sensor parameters of the static        sensors,        b) a time stamp generator, which is adapted to generate a time        stamp,        c) a second interface, which is adapted to read in second sensor        positions and sensor parameters of sensors, which are coupled to        the autonomous vehicles, and to provide the second sensor        positions in coordinates of the global coordinate system,        d) a sensor data generator, which is adapted to generate sensor        data for the static sensors and the sensors of the autonomous        vehicles as a function of the respective sensor parameters and        the respective first or second sensor positions and taking into        account the environment information in coordinates of the global        coordinate system, wherein the generated time stamp is assigned        to the generated sensor data,        e) a selection module, which is adapted to select at least one        of the autonomous vehicles,        f) a transformation module, which is adapted to transform the        generated sensor data into a local coordinate system of the        selected autonomous vehicle,        and        g) a transmission module, which is adapted to transmit the        transformed sensor data to a controller of the selected        autonomous vehicle for controlling the selected autonomous        vehicle in the environment as a function of the transformed        sensor data.

The apparatus can be coupled, in particular, to the controller of theselected autonomous vehicle, for example by a communications network, inorder to transfer the sensor data.

Furthermore, embodiments of the invention relates to a computer programproduct (non-transitory computer readable storage medium havinginstructions, which when executed by a processor, perform actions),which can be directly loaded into a programmable computer, comprisingprogram code components, which on execution of the program by a computercause it to carry out the steps of an inventive method.

A computer program product can be provided or supplied for example on astorage medium, such as for example memory card, USB stick, CD-ROM, DVD,a non-volatile/permanent storage medium (non-transitory storage medium)or also in the form of a data file downloadable from a server in anetwork.

BRIEF DESCRIPTION

Some of the embodiments will be described in detail, with reference tothe following figures, wherein like designations denote like members,wherein:

FIG. 1 : shows a first exemplary embodiment of an inventive method;

FIG. 2 : shows a first exemplary embodiment of an inventive apparatus;

FIG. 3 : shows a second exemplary embodiment of an inventive apparatus;

FIG. 4 : shows a second exemplary embodiment of an inventive method; and

FIG. 5 : shows a third exemplary embodiment of an inventive method.

Mutually corresponding parts are provided with identical referencenumerals in all figures.

DETAILED DESCRIPTION

In particular, the following exemplary embodiments show only exemplaryimplementation options as to how, in particular, such implementations ofthe inventive teaching could look since it is not possible or expedientor necessary to cite all of these implementation options for anunderstanding of embodiments of the invention.

In particular, these customary implementation variants known to theperson skilled in the art can be achieved solely by hardware(components) or solely by software (components). Alternatively and/or inaddition, as part of his expert skill the person skilled in the art canselect largely any inventive combinations of hardware (components) andsoftware (components) in order to realize inventive implementationvariants.

FIG. 1 shows a flow diagram as a first exemplary embodiment of aninventive method for generating sensor data for controlling anautonomous vehicle in an environment. For example, controlling anautonomous vehicle in an environment can be simulated in acomputer-aided manner. The environment can be, for example, a buildingor a transport infrastructure. A large number of autonomous vehicles(AVs), such as autonomous transport vehicles (Automated Guided Vehicles,AGVs) may be situated in the environment in embodiments.

Sensor data, which can be used to control one of these autonomousvehicles, is to be generated. The autonomous vehicle can be (virtually)controlled, for example in a simulated environment, on the basis of thesensor data, in other words the movement and/or control of theautonomous vehicle in the environment can be simulated in acomputer-aided manner on the basis of the generated sensor data.Alternatively, the generated sensor data can also be used to control theautonomous vehicle in the real environment.

In the first step S1, an environment model of the environment is readin. For example, the environment model is a BIM model of the building.The environment model has a global coordinate system, in other words inparticular positions of environmental features, such as for exampleroutes, walls, obstacles, etc., are given in this coordinate system andstored in the environment model. In addition the environment modelcomprises first sensor positions of static sensors, such as permanentlyinstalled cameras for example, in the environment and the environmentmodel comprises sensor parameters of the static sensors. A static sensoris in particular immobile, in other words its sensor position and/or itsfield of vision may not change in embodiments. A sensor parameter canbe, for example, a field of vision, a sensitivity, a sensor type, aresolution, etc. One sensor parameter may be uniquely assigned to onerespective sensor in embodiments.

In embodiments, the environment model comprises all sensor informationfrom the static sensors in the environment, such as of the building orin the building for example.

In the next step S2, a time stamp is generated. In embodiments, thefollowing steps S3 to S7 are carried out for this time stamp, in otherwords when the time stamp is updated, the steps are repeated for theupdated time stamp.

In the next step S3, second sensor positions and sensor parameters ofsensors, which are coupled to the autonomous vehicles, are read in. Theautonomous vehicles may comprise sensors, such as cameras or distancemeasuring devices for example, which are attached, for example, to arespective autonomous vehicle. The sensor positions of the respectivesensors are thus dependent on the positions of the respective vehicles.The sensors of the autonomous vehicles can also be referred to as movingsensors since the sensor position and/or the field of vision of thesesensors can change with time. The sensor positions of the sensors of theautonomous vehicles and/or the positions of the autonomous vehicles areprovided for the generated time stamp in coordinates of the globalcoordinate system.

In the next step S4, sensor data is generated for the static sensors andthe (moving) sensors of the autonomous vehicles as a function of therespective sensor parameters of the respective sensors and as a functionof the respective first sensor positions of the static sensors or thesecond sensor positions of the moving sensors in coordinates of theglobal coordinate system and taking into account the environmentinformation. In addition, the generated time stamp is assigned to thegenerated sensor data. In other words, the sensor data is generated forthis time increment and receives the generated time stamp.

For example, sensor data is generated for a camera permanently installedon an autonomous vehicle, in other words data is generated, which thecamera outputs as a function of the camera position, the camera field ofview, the camera sensitivity, etc. In particular environmentinformation, such as an obstacle for example, is taken into account inthe generation of the sensor data. Consequently, the generated cameradata can comprise the obstacle or information relating to the obstacle,if at the given time the obstacle is situated in the camera field ofvision.

Sensor data is generated for all existing sensors, with environmentinformation of the environment from the environment model being takeninto account. In addition, position information of the autonomousvehicles can be taken into account in the generation of the sensor data.

In embodiments, the sensor data is centrally generated and provided. Thesensor data can be generated, for example centrally, by means of asensor simulation.

In the next step S5, at least one of the autonomous vehicles isselected. The selection can be made on the basis of a predefined order,on the basis of a selection criterion or randomly.

In the next step S6, the generated sensor data is transformed into alocal coordinate system of the selected autonomous vehicle. Thus, forexample, sensor data from a static camera is transformed into thecoordinate system of the selected vehicle, so this data is present fromthe perspective of the vehicle.

In the next step S7, the transformed sensor data is transferred to acontroller of the selected autonomous vehicle.

In the next step S8, the selected vehicle can be controlled as afunction of the transformed sensor data in the environment.Alternatively or in addition, the transformed sensor data can be usedfor training artificial intelligence, such as an artificial neuralnetwork for example, which is adapted, for example, to aid control of anautonomous vehicle. Thus on the basis of the generated sensor data, amachine learning method can be trained, which can make betterpredictions for planning combined with the simulation and adjusts theevents and habits of a specific environment. For example, a hybridmodel-predictive-control approach consisting of artificial intelligenceand a simulation model can be used.

FIG. 2 shows in a schematic representation an exemplary embodiment of aninventive apparatus 100 for generating sensor data for controlling anautonomous vehicle AGV1 in an environment. For example a large number ofautonomous vehicles is moving in the environment, for example a factoryor a transport infrastructure. Sensor data is necessary to control theautonomous vehicles. The apparatus 100 is adapted to centrally generateand output such sensor data for the autonomous vehicles AGV.

The apparatus 100 can generate sensor data by means of a sensorsimulation SIM for example. This generated or simulated sensor data canbe used for example for a computer-aided simulation of the movementand/or control of the autonomous vehicles, such as for a virtualcommissioning of the autonomous vehicles for example.

Alternatively, the apparatus 100 can generate and provide sensor datacentrally for the autonomous vehicles AGV. For this embodiment, theapparatus 100 can be linked to the autonomous vehicles AGV and/or thestatic sensors in the environment via a 5G communications network.

The apparatus 100 comprises a first interface IF1, a second interfaceIF2, a time stamp generator TGEN, a sensor data generator SGEN, aselection module SEL, a transformation module TRANS and a transmissionmodule OUT.

The first interface IF1 is adapted to read in an environment model BIMof the environment, with the environment model BIM having a globalcoordinate system and the environment model comprising first sensorpositions POS1 of static sensors in the environment and environmentinformation in this global coordinate system and sensor parameters PAR1of these static sensors. The environment model can be, for example, adigital information model of the environment. For example, theenvironment model is a BIM model if the environment is abuilding/structure. For example, it can be a production plant in whichAGVs move.

The time stamp generator TGEN is adapted to generate a global time stampTS. The time stamp TS is required to assign a unique global time togenerated sensor data. The time stamp can be updated in predefined timeincrements. When the time stamp TS is updated the sensor data isgenerated anew for the updated time stamp.

The second interface IF2 is adapted to read in second sensor positionsPOS2 and sensor parameters PAR2 from sensors, which are coupled to theautonomous vehicles AGV, and to provide the second sensor positions incoordinates of the global coordinate system.

The sensor data generator SGEN is adapted to generate sensor data DATAfor the static sensors and the sensors of the autonomous vehicles as afunction of the respective sensor parameters PAR1, PAR2 and therespective first or second sensor positions POS1, POS2 in coordinates ofthe global coordinate system, with the generated time stamp TS beingassigned to the generated sensor data.

The sensor data DATA can be generated for example by means of acomputer-aided sensor simulation SIM. In addition, a quality of thegenerated sensor data can be reduced and provided in such a way. Thequality of the simulated sensor data can consequently emulate arealistic quality of real data.

In particular, at least one environmental feature can be detected on thebasis of the generated sensor data. An environmental feature can be, forexample, an obstacle, a wall, a bottleneck, etc. An environmentalfeature can also be a person, who is moving in the environment. Aposition of the environmental feature can thus be provided incoordinates of the global coordinate system.

The selection module SEL is adapted to select at least one of theautonomous vehicles and to transfer information I1 about this selectionto the transformation module TRANS.

The transformation module TRANS is adapted to transform the generatedsensor data DATA and/or the detected environmental features into a localcoordinate system of the selected autonomous vehicle AGV1. In particulara transmission latency, which occurs on a transfer of the sensor data tothe selected vehicle, can be taken into account in the coordinatetransformation. For the transformation, the information I1 about theselected vehicle and at least the position of the selected vehicle forthe applicable time stamp is transferred to the transformation moduleTRANS. In embodiments, the transformation module TRANS transforms allsensor data DATA into the local coordinate system of the selectedautonomous vehicle AGV1, which is relevant to the selected vehicle AGV1.For example, the generated sensor data DATA can be filtered as afunction of the position of the selected autonomous vehicle in theglobal coordinate system and only the filtered sensor data DATA istransformed into the local coordinate system of the selected autonomousvehicle AGV1 and transferred to the selected autonomous vehicle AGV1.

The sensor data DATA can in particular also be transformed into thelocal coordinate system of the selected autonomous vehicle as a functionof previously generated further sensor data, to which a preceding timestamp is assigned. For example, new sensor data can be extrapolated forthe current time stamp on the basis of the previously generated sensordata.

If an environmental feature was detected on the basis of the sensordata, in particular only the environmental feature can be transformedinto the local coordinate system.

The transmission module OUT is adapted to transfer the transformedsensor data DATA* to a controller of the selected autonomous vehicleAGV1 for controlling the selected autonomous vehicle in the environmentas a function of the transformed sensor data DATA*.

FIG. 3 shows in a schematic representation a further exemplaryembodiment of an inventive apparatus 100 for generating sensor data forcontrolling an autonomous vehicle in an environment in which at leastone further autonomous vehicle is situated. For example, it is a factoryin which autonomous transport vehicles AGV1, AGV2, AGV3, AGV4 move.

The apparatus 100 can be adapted in particular for generating sensordata by means of a sensor simulation. FIG. 2 shows a logical structureof such a simulation system. The sensor data is simulated not from theperspective of an individual autonomous transport vehicle AGV1, AGV2,AGV3, AGV4, but centrally for all guided vehicles simultaneously. Inembodiments, the autonomous vehicles AGV1, AGV2, AGV3, AGV4 may becoupled to the apparatus 100 for this purpose.

In embodiments, there is a BIM model for the factory. The sensorpositions of statically installed sensors CAM1, CAM2, and the type ofthe respective sensor and further parameters, such as visible regions ofa camera CAM1, CAM2 for example, can be determined from the BIM model.The BIM model is also used to define a global coordinate system in whichthe autonomous transport vehicles AGV1, AGV2, AGV3, AGV4 can move. Inembodiments, the autonomous transport vehicles can access the same datawithout a relatively long delay. This can be achieved for example by wayof a broadband 5G network in the factory environment. Alternatively orin addition, any time delay can also be simulatively generated and takeninto account as soon as the data is converted into the perspective of anindividual AGV to be controlled.

Firstly, all sensor data of the static sensors CAM1, CAM2 and the movingsensors S11, S12, S21, . . . S23, S31, S32, S41, . . . S43 of theautonomous transport vehicles AGV1, AGV4 is generated with a global timestamp. The time stamp can be increased in discrete increments dependingon how accurately the required temporal resolution was defined (forexample based on the speed of the AGVs or other components of aproduction plant). In addition, the respective sensor positions of thesensors are stored in a global coordinate system. A sensor position of amoving sensor can change, for example, from time stamp to time stamp,for example if the sensor is mounted, for example, on a moving guidedvehicle.

The generated sensor data can be distributed to all autonomous vehiclesAGV1, AGV4 that are part of the sensor network. For this, the sensordata is transformed into a local coordinate system of the respectiveautonomous vehicle. The distribution can also be limited to thevehicles, which are potentially influenced by the data, however. Aninfluence may be estimated for example on the basis of the BIM model.If, for example, a distance of a guided vehicle from a sensor is above aparticular threshold value, the data of this sensor can thus initiallybe ignored. In the case of an influence, the data is transformed intothe local coordinate system of the guided vehicle being considered. Aparticular latency time can also be included for this. This thenresults, for example, in an extrapolation of a movement in the future.The transformed data is then transferred to the guided vehicle currentlybeing considered and can be evaluated by it.

FIG. 4 shows in a schematic representation a further exemplaryembodiment of an inventive method.

A building, such as a production plant for example, is shown in whichtwo autonomous transport vehicles AGV1, AGV2 and a person PER can move.The transport vehicles AGV1, AGV2 each comprise at least a sensor S11,S21, such as a camera for example. The building also comprises astatically installed camera CAM1. The respective cameras have apredefined field of vision.

The two autonomous vehicles AGV1, AGV2 are to be virtually commissioned,in other words a computer-aided simulation of a movement and/or behaviorof the autonomous vehicles AGV1, AGV2 in the building is to be carriedout by taking into account the building features H and/or the movementof the person PER. For this, simulated sensor data of the existingsensors is required in order, for example, to simulate a reaction of oneof the autonomous vehicles to the person PER.

For this, sensor data is centrally simulated on the basis of theinformation of a digital building model of the building for all staticand moving sensors at a predefined time. The position of the person PERand/or the autonomous vehicles AGV1, AGV2 is taken into account. Aglobal time stamp is defined for this.

For the generation of sensor data the sensor positions of the staticsensor CAM1 and the sensors S11, S21 of the autonomous vehicles AGV1,AGV2 are read in and provided in a global coordinate system predefinedby the building model. Sensor data is then generated as a function ofthe respective sensor parameters and the respective sensor positions andtaking into account the building features H and/or the person PER incoordinates of the global coordinate system, with the generated timestamp being assigned to the generated sensor data.

The generated sensor data can then be transformed into a localcoordinate system of the first autonomous vehicle AGV1 and transferredto it. The first autonomous vehicle AGV1 thus also receives sensor datafrom the sensor S21 of the second autonomous vehicle AGV2. AGV1 can thusbe controlled as a function of the transformed sensor data. For example,AGV1 thus also receives information about the person PER who, behind anobstacle H, is not detected by the sensor S11 of the first autonomousvehicle AGV1. This additional sensor data can be taken into accountduring the control/virtual commissioning of the first autonomous vehicleAGV1 a.

FIG. 5 shows in a schematic representation a further exemplaryembodiment of the inventive method.

A production plant is shown, which comprises two production machines M1,M2, and two autonomous transport vehicles AGV1, AGV2.

Embodiments of the invention enable, for example, distributedwarehousing or a simulation of distributed warehousing, which is managedwith autonomous transport vehicles AGV1, AGV2. Sensors of the autonomousvehicles AGV1, AGV2 can detect, for example, a stock level at differentmachines M1, M2.

For example, a first transport vehicle AGV1 can pick up a full stocklevel at a first machine M1 via the sensors of the second autonomoustransport vehicle AGV2. This is made possible by way of a centralgeneration of sensor data of all sensors in a global coordinate systemwith subsequent transformation of the generated sensor data into a localcoordinate system of the first autonomous transport vehicle.

Thus, while the second autonomous transport vehicle AGV2 has no sparetransport capacities, the first autonomous transport vehicle AGV1 canprevent overflowing of the stock level of the first machine M1 andproduction can thus proceed without interruption.

Although the present invention has been disclosed in the form ofpreferred embodiments and variations thereon, it will be understood thatnumerous additional modifications and variations could be made theretowithout departing from the scope of the invention.

For the sake of clarity, it is to be understood that the use of “a” or“an” throughout this application does not exclude a plurality, and“comprising” does not exclude other steps or elements.

1. A computer-implemented method for generating sensor data forcontrolling an autonomous vehicle in an environment in which at leastone further autonomous vehicle is situated, with the following methodsteps: a) reading in an environment model of the environment, whereinthe environment model has a global coordinate system and the environmentmodel comprises first sensor positions of static sensors in theenvironment and environment information in this global coordinate systemand sensor parameters of the static sensors, b) generating a time stamp,c) reading in second sensor positions and sensor parameters of sensors,which are coupled to the autonomous vehicles, and providing the secondsensor positions in coordinates of the global coordinate system, d)generating sensor data for the static sensors and the sensors of theautonomous vehicles as a function of the respective sensor parametersand the respective first or second sensor positions and taking intoaccount the environment information in coordinates of the globalcoordinate system, wherein the generated time stamp is assigned to thegenerated sensor data, e) selecting at least one of the autonomousvehicles, f) transforming the generated sensor data into a localcoordinate system of the selected autonomous vehicle, and g)transmitting the transformed sensor data to a controller of the selectedautonomous vehicle for controlling the selected autonomous vehicle inthe environment as a function of the transformed sensor data.
 2. Thecomputer-implemented method as claimed in claim 1, wherein theenvironment model is a computer-readable building model.
 3. Thecomputer-implemented method as claimed in claim 1, wherein the sensordata for the static sensors and the sensors of the autonomous vehiclesare centrally generated for the time stamp.
 4. The computer-implementedmethod as claimed in claim 1, wherein the time stamp is updated inpredefined time increments and the sensor data is generated anew for theupdated time stamp.
 5. The computer-implemented method as claimed inclaim 1, wherein the sensor data is transformed into the localcoordinate system of the selected autonomous vehicle by taking intoaccount a transmission latency for transmitting the sensor data to theselected autonomous vehicle.
 6. The computer-implemented method asclaimed in claim 1, wherein the sensor data is transformed into thelocal coordinate system of the selected autonomous vehicle as a functionof generated sensor data, to which a preceding time stamp is assigned.7. The computer-implemented method as claimed in claim 1, wherein thesensor data is generated by means of a computer-aided sensor simulation.8. The computer-implemented method as claimed in claim 1, wherein aquality of at least some of the generated sensor data is reduced andmodified sensor data with reduced quality is transformed into the localcoordinate system of the selected autonomous vehicle.
 9. Thecomputer-implemented method as claimed in claim 1, wherein on the basisof the generated sensor data at least one environmental feature isdetected and only the detected environmental feature is transformed intothe local coordinate system of the selected autonomous vehicle.
 10. Thecomputer-implemented method as claimed in claim 1, wherein the generatedsensor data is filtered as a function of the position of the selectedautonomous vehicle in the global coordinate system and only the filteredsensor data is transformed into the local coordinate system of theselected autonomous vehicle and transferred to the selected autonomousvehicle.
 11. The computer-implemented method as claimed in claim 1,wherein the static sensors and the sensors of the autonomous vehiclesand/or the autonomous vehicles are connected together by acommunications network.
 12. The computer-implemented method as claimedin claim 1, wherein the selected autonomous vehicle is controlled in asimulation environment as a function of the transformed sensor data. 13.The computer-implemented method as claimed in claim 1, wherein theenvironment is a factory and the autonomous vehicles are driverlesstransport vehicles.
 14. An apparatus for generating sensor data forcontrolling an autonomous vehicle in an environment in which least onefurther autonomous vehicle is situated, wherein the apparatus comprises:a) a first interface, which is adapted to read in an environment modelof the environment, wherein the environment model has a globalcoordinate system and the environment model comprises first sensorpositions of static sensors in the environment and environmentinformation in this global coordinate system and sensor parameters ofthe static sensors, b) a time stamp generator, which is adapted togenerate a time stamp, c) a second interface, which is adapted to readin second sensor positions and sensor parameters of sensors, which arecoupled to the autonomous vehicles, and to provide the second sensorpositions in coordinates of the global coordinate system, d) a sensordata generator, which is adapted to generate sensor data for the staticsensors and the sensors of the autonomous vehicles as a function of therespective sensor parameters and the respective first or second sensorpositions and taking into account the environment information incoordinates of the global coordinate system, wherein the generated timestamp is assigned to the generated sensor data, e) a selection module,which is adapted to select at least one of the autonomous vehicles, f) atransformation module, which is adapted to transform the generatedsensor data into a local coordinate system of the selected autonomousvehicle, and g) a transmission module, which is adapted to transmit thetransformed sensor data to a controller of the selected autonomousvehicle for controlling the selected autonomous vehicle in theenvironment as a function of the transformed sensor data.
 15. A computerprogram product comprising a computer readable hardware storage devicehaving computer readable program code stored therein, said program codeexecutable by a processor of a computer system to implement the methodas claimed in claim 1.